算法：

定位车牌

通过对输出图片进行一系列的处理后，筛选出矩形区域

1

if type(car_pic) == type(""):

img = imreadex(car_pic)

else:

img = car_pic

pic_hight, pic_width = img.shape[:2]

if pic_width > MAX_WIDTH:

resize_rate = MAX_WIDTH / pic_width

img = cv2.resize(img, (MAX_WIDTH, int(pic_hight * resize_rate)), interpolation=cv2.INTER_AREA)

blur = self.cfg["blur"]

# 高斯去噪

if blur > 0:

img = cv2.GaussianBlur(img, (blur, blur), 0) # 图片分辨率调整

oldimg = img

gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #灰度处理

Sobel_x = cv2.Sobel(gray_image, cv2.CV_16S, 1, 0) #sobel算子边缘检测

absX = cv2.convertScaleAbs(Sobel_x) #转回uint8

image = absX

ret, image = cv2.threshold(image, 0, 255, cv2.THRESH_OTSU) #自适应阈值处理

kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (14, 5)) #闭运算，白色部分练成整体

image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernelX, iterations=1)

kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (20, 1)) #去除小白点

kernelY = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 19))

image = cv2.dilate(image, kernelX) #膨胀

image = cv2.erode(image, kernelX) #腐蚀

image = cv2.erode(image, kernelY) #腐蚀

image = cv2.dilate(image, kernelY) #膨胀

image = cv2.medianBlur(image, 15) #中值滤波去除噪点

contours, hierarchy = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)#轮廓检测

# 一一排除不是车牌的矩形区域

car_contours = [] #筛选车牌位置的轮廓

for cnt in contours:

rect = cv2.minAreaRect(cnt)

area_width, area_height = rect[1]

if area_width < area_height:

area_width, area_height = area_height, area_width

wh_ratio = area_width / area_height

# print(wh_ratio)

# 要求矩形区域长宽比在2到5.5之间，2到5.5是车牌的长宽比，其余的矩形排除

if wh_ratio > 2 and wh_ratio < 5.5:

car_contours.append(rect)

box = cv2.boxPoints(rect)

box = np.int0(box)

print("精确定位")

矫正矩形
XM返佣https://www.kaifx.cn/broker/xm.html

card_imgs = []

# 矩形区域可能是倾斜的矩形，需要矫正，以便使用颜色定位

for rect in car_contours:

if rect[2] > -1 and rect[2] < 1: # 创造角度，使得左、高、右、低拿到正确的值

angle = 1

else:

angle = rect[2]

rect = (rect[0], (rect[1][0] + 5, rect[1][1] + 5), angle) # 扩大范围，避免车牌边缘被排除

box = cv2.boxPoints(rect)

heigth_point = right_point = [0, 0]

left_point = low_point = [pic_width, pic_hight]

for point in box:

if left_point[0] > point[0]:

left_point = point

if low_point[1] > point[1]:

low_point = point

if heigth_point[1] < point[1]:

heigth_point = point

if right_point[0] < point[0]:

right_point = point

if left_point[1] <= right_point[1]: # 正角度

new_right_point = [right_point[0], heigth_point[1]]

pts2 = np.float32([left_point, heigth_point, new_right_point]) # 字符只是高度需要改变

pts1 = np.float32([left_point, heigth_point, right_point])

M = cv2.getAffineTransform(pts1, pts2)

dst = cv2.warpAffine(oldimg, M, (pic_width, pic_hight))

point_limit(new_right_point)

point_limit(heigth_point)

point_limit(left_point)

card_img = dst[int(left_point[1]):int(heigth_point[1]), int(left_point[0]):int(new_right_point[0])]

card_imgs.append(card_img)

elif left_point[1] > right_point[1]: # 负角度

new_left_point = [left_point[0], heigth_point[1]]

pts2 = np.float32([new_left_point, heigth_point, right_point]) # 字符只是高度需要改变

pts1 = np.float32([left_point, heigth_point, right_point])

M = cv2.getAffineTransform(pts1, pts2)

dst = cv2.warpAffine(oldimg, M, (pic_width, pic_hight))

point_limit(right_point)

point_limit(heigth_point)

point_limit(new_left_point)

card_img = dst[int(right_point[1]):int(heigth_point[1]), int(new_left_point[0]):int(right_point[0])]

card_imgs.append(card_img)

颜色定位

colors = []

for card_index, card_img in enumerate(card_imgs):

green = yello = blue = black = white = 0

card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV)

# 有转换失败的可能，原因来自于上面矫正矩形出错

if card_img_hsv is None:

continue

row_num, col_num = card_img_hsv.shape[:2]

card_img_count = row_num * col_num

for i in range(row_num):

for j in range(col_num):

H = card_img_hsv.item(i, j, 0)

S = card_img_hsv.item(i, j, 1)

V = card_img_hsv.item(i, j, 2)

if 11 < H <= 34 and S > 34: # 图片分辨率调整

yello += 1

elif 35 < H <= 99 and S > 34: # 图片分辨率调整

green += 1

elif 99 < H <= 124 and S > 34: # 图片分辨率调整

blue += 1

if 0 < H < 180 and 0 < S < 255 and 0 < V < 46:

black += 1

elif 0 < H < 180 and 0 < S < 43 and 221 < V < 225:

white += 1

color = "no"

limit1 = limit2 = 0

if yello * 2 >= card_img_count:

color = "yello"

limit1 = 11

limit2 = 34 # 有的图片有色偏偏绿

elif green * 2 >= card_img_count:

color = "green"

limit1 = 35

limit2 = 99

elif blue * 2 >= card_img_count:

color = "blue"

limit1 = 100

limit2 = 124 # 有的图片有色偏偏紫

elif black + white >= card_img_count * 0.7: # TODO

color = "bw"

print(color)

colors.append(color)

print(blue, green, yello, black, white, card_img_count)

if limit1 == 0:

continue

识别车牌字符

predict_result = []

word_images = []

roi = None

card_color = None

for i, color in enumerate(colors):

if color in ("blue", "yello", "green"):

card_img = card_imgs[i] # 定位的车牌

gray_img = cv2.cvtColor(card_img, cv2.COLOR_BGR2GRAY)

# 黄、绿车牌字符比背景暗、与蓝车牌刚好相反，所以黄、绿车牌需要反向

if color == "green" or color == "yello":

gray_img = cv2.bitwise_not(gray_img)

ret, gray_img = cv2.threshold(gray_img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

# 查找水平直方图波峰

x_histogram = np.sum(gray_img, axis=1)

x_min = np.min(x_histogram)

x_average = np.sum(x_histogram) / x_histogram.shape[0]

x_threshold = (x_min + x_average) / 2

wave_peaks = find_waves(x_threshold, x_histogram)

if len(wave_peaks) == 0:

print("peak less 0:")

continue

# 认为水平方向，最大的波峰为车牌区域

wave = max(wave_peaks, key=lambda x: x[1] - x[0])

gray_img = gray_img[wave[0]:wave[1]]

# 查找垂直直方图波峰

row_num, col_num = gray_img.shape[:2]

# 去掉车牌上下边缘1个像素，避免白边影响阈值判断

gray_img = gray_img[1:row_num - 1]

y_histogram = np.sum(gray_img, axis=0)

y_min = np.min(y_histogram)

y_average = np.sum(y_histogram) / y_histogram.shape[0]

y_threshold = (y_min + y_average) / 5 # U和0要求阈值偏小，否则U和0会被分成两半

wave_peaks = find_waves(y_threshold, y_histogram)

# for wave in wave_peaks:

# cv2.line(card_img, pt1=(wave[0], 5), pt2=(wave[1], 5), color=(0, 0, 255), thickness=2)

# 车牌字符数应大于6

if len(wave_peaks) <= 6:

print("peak less 1:", len(wave_peaks))

continue

wave = max(wave_peaks, key=lambda x: x[1] - x[0])

max_wave_dis = wave[1] - wave[0]

# 判断是否是左侧车牌边缘

if wave_peaks[0][1] - wave_peaks[0][0] < max_wave_dis / 3 and wave_peaks[0][0] == 0:

wave_peaks.pop(0)

# 组合分离汉字

cur_dis = 0

for i, wave in enumerate(wave_peaks):

if wave[1] - wave[0] + cur_dis > max_wave_dis * 0.6:

break

else:

cur_dis += wave[1] - wave[0]

if i > 0:

wave = (wave_peaks[0][0], wave_peaks[i][1])

wave_peaks = wave_peaks[i + 1:]

wave_peaks.insert(0, wave)

# 去除车牌上的分隔点

point = wave_peaks[2]

if point[1] - point[0] < max_wave_dis / 3:

point_img = gray_img[:, point[0]:point[1]]

if np.mean(point_img) < 255 / 5:

wave_peaks.pop(2)

if len(wave_peaks) <= 6:

print("peak less 2:", len(wave_peaks))

continue

part_cards = seperate_card(gray_img, wave_peaks)

for i, part_card in enumerate(part_cards):

# 可能是固定车牌的铆钉

if np.mean(part_card) < 255 / 5:

print("a point")

continue

part_card_old = part_card

w = abs(part_card.shape[1] - SZ) // 2

part_card = cv2.copyMakeBorder(part_card, 0, 0, w, w, cv2.BORDER_CONSTANT,

value=[0, 0, 0])

part_card = cv2.resize(part_card, (SZ, SZ), interpolation=cv2.INTER_AREA)

word_images.append(part_card)

word_images_ = word_images.copy()

predict_result = template_matching(word_images_)

roi = card_img

card_color = color

print(predict_result)

break

算法：

定位车牌

通过对输出图片进行一系列的处理后，筛选出矩形区域

1

if type(car_pic) == type(""):

img = imreadex(car_pic)

else:

img = car_pic

pic_hight, pic_width = img.shape[:2]

if pic_width > MAX_WIDTH:

resize_rate = MAX_WIDTH / pic_width

img = cv2.resize(img, (MAX_WIDTH, int(pic_hight * resize_rate)), interpolation=cv2.INTER_AREA)

blur = self.cfg["blur"]

# 高斯去噪

if blur > 0:

img = cv2.GaussianBlur(img, (blur, blur), 0) # 图片分辨率调整

oldimg = img

gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #灰度处理

Sobel_x = cv2.Sobel(gray_image, cv2.CV_16S, 1, 0) #sobel算子边缘检测

absX = cv2.convertScaleAbs(Sobel_x) #转回uint8

image = absX

ret, image = cv2.threshold(image, 0, 255, cv2.THRESH_OTSU) #自适应阈值处理

kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (14, 5)) #闭运算，白色部分练成整体

image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernelX, iterations=1)

kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (20, 1)) #去除小白点

kernelY = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 19))

image = cv2.dilate(image, kernelX) #膨胀

image = cv2.erode(image, kernelX) #腐蚀

image = cv2.erode(image, kernelY) #腐蚀

image = cv2.dilate(image, kernelY) #膨胀

image = cv2.medianBlur(image, 15) #中值滤波去除噪点

contours, hierarchy = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)#轮廓检测

# 一一排除不是车牌的矩形区域

car_contours = [] #筛选车牌位置的轮廓

for cnt in contours:

rect = cv2.minAreaRect(cnt)

area_width, area_height = rect[1]

if area_width < area_height:

area_width, area_height = area_height, area_width

wh_ratio = area_width / area_height

# print(wh_ratio)

# 要求矩形区域长宽比在2到5.5之间，2到5.5是车牌的长宽比，其余的矩形排除

if wh_ratio > 2 and wh_ratio < 5.5:

car_contours.append(rect)

box = cv2.boxPoints(rect)

box = np.int0(box)

print("精确定位")

矫正矩形
XM返佣https://www.kaifx.cn/broker/xm.html

card_imgs = []

# 矩形区域可能是倾斜的矩形，需要矫正，以便使用颜色定位

for rect in car_contours:

if rect[2] > -1 and rect[2] < 1: # 创造角度，使得左、高、右、低拿到正确的值

angle = 1

else:

angle = rect[2]

rect = (rect[0], (rect[1][0] + 5, rect[1][1] + 5), angle) # 扩大范围，避免车牌边缘被排除

box = cv2.boxPoints(rect)

heigth_point = right_point = [0, 0]

left_point = low_point = [pic_width, pic_hight]

for point in box:

if left_point[0] > point[0]:

left_point = point

if low_point[1] > point[1]:

low_point = point

if heigth_point[1] < point[1]:

heigth_point = point

if right_point[0] < point[0]:

right_point = point

if left_point[1] <= right_point[1]: # 正角度

new_right_point = [right_point[0], heigth_point[1]]

pts2 = np.float32([left_point, heigth_point, new_right_point]) # 字符只是高度需要改变

pts1 = np.float32([left_point, heigth_point, right_point])

M = cv2.getAffineTransform(pts1, pts2)

dst = cv2.warpAffine(oldimg, M, (pic_width, pic_hight))

point_limit(new_right_point)

point_limit(heigth_point)

point_limit(left_point)

card_img = dst[int(left_point[1]):int(heigth_point[1]), int(left_point[0]):int(new_right_point[0])]

card_imgs.append(card_img)

elif left_point[1] > right_point[1]: # 负角度

new_left_point = [left_point[0], heigth_point[1]]

pts2 = np.float32([new_left_point, heigth_point, right_point]) # 字符只是高度需要改变

pts1 = np.float32([left_point, heigth_point, right_point])

M = cv2.getAffineTransform(pts1, pts2)

dst = cv2.warpAffine(oldimg, M, (pic_width, pic_hight))

point_limit(right_point)

point_limit(heigth_point)

point_limit(new_left_point)

card_img = dst[int(right_point[1]):int(heigth_point[1]), int(new_left_point[0]):int(right_point[0])]

card_imgs.append(card_img)

颜色定位

colors = []

for card_index, card_img in enumerate(card_imgs):

green = yello = blue = black = white = 0

card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV)

# 有转换失败的可能，原因来自于上面矫正矩形出错

if card_img_hsv is None:

continue

row_num, col_num = card_img_hsv.shape[:2]

card_img_count = row_num * col_num

for i in range(row_num):

for j in range(col_num):

H = card_img_hsv.item(i, j, 0)

S = card_img_hsv.item(i, j, 1)

V = card_img_hsv.item(i, j, 2)

if 11 < H <= 34 and S > 34: # 图片分辨率调整

yello += 1

elif 35 < H <= 99 and S > 34: # 图片分辨率调整

green += 1

elif 99 < H <= 124 and S > 34: # 图片分辨率调整

blue += 1

if 0 < H < 180 and 0 < S < 255 and 0 < V < 46:

black += 1

elif 0 < H < 180 and 0 < S < 43 and 221 < V < 225:

white += 1

color = "no"

limit1 = limit2 = 0

if yello * 2 >= card_img_count:

color = "yello"

limit1 = 11

limit2 = 34 # 有的图片有色偏偏绿

elif green * 2 >= card_img_count:

color = "green"

limit1 = 35

limit2 = 99

elif blue * 2 >= card_img_count:

color = "blue"

limit1 = 100

limit2 = 124 # 有的图片有色偏偏紫

elif black + white >= card_img_count * 0.7: # TODO

color = "bw"

print(color)

colors.append(color)

print(blue, green, yello, black, white, card_img_count)

if limit1 == 0:

continue

识别车牌字符

predict_result = []

word_images = []

roi = None

card_color = None

for i, color in enumerate(colors):

if color in ("blue", "yello", "green"):

card_img = card_imgs[i] # 定位的车牌

gray_img = cv2.cvtColor(card_img, cv2.COLOR_BGR2GRAY)

# 黄、绿车牌字符比背景暗、与蓝车牌刚好相反，所以黄、绿车牌需要反向

if color == "green" or color == "yello":

gray_img = cv2.bitwise_not(gray_img)

ret, gray_img = cv2.threshold(gray_img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

# 查找水平直方图波峰

x_histogram = np.sum(gray_img, axis=1)

x_min = np.min(x_histogram)

x_average = np.sum(x_histogram) / x_histogram.shape[0]

x_threshold = (x_min + x_average) / 2

wave_peaks = find_waves(x_threshold, x_histogram)

if len(wave_peaks) == 0:

print("peak less 0:")

continue

# 认为水平方向，最大的波峰为车牌区域

wave = max(wave_peaks, key=lambda x: x[1] - x[0])

gray_img = gray_img[wave[0]:wave[1]]

# 查找垂直直方图波峰

row_num, col_num = gray_img.shape[:2]

# 去掉车牌上下边缘1个像素，避免白边影响阈值判断

gray_img = gray_img[1:row_num - 1]

y_histogram = np.sum(gray_img, axis=0)

y_min = np.min(y_histogram)

y_average = np.sum(y_histogram) / y_histogram.shape[0]

y_threshold = (y_min + y_average) / 5 # U和0要求阈值偏小，否则U和0会被分成两半

wave_peaks = find_waves(y_threshold, y_histogram)

# for wave in wave_peaks:

# cv2.line(card_img, pt1=(wave[0], 5), pt2=(wave[1], 5), color=(0, 0, 255), thickness=2)

# 车牌字符数应大于6

if len(wave_peaks) <= 6:

print("peak less 1:", len(wave_peaks))

continue

wave = max(wave_peaks, key=lambda x: x[1] - x[0])

max_wave_dis = wave[1] - wave[0]

# 判断是否是左侧车牌边缘

if wave_peaks[0][1] - wave_peaks[0][0] < max_wave_dis / 3 and wave_peaks[0][0] == 0:

wave_peaks.pop(0)

# 组合分离汉字

cur_dis = 0

for i, wave in enumerate(wave_peaks):

if wave[1] - wave[0] + cur_dis > max_wave_dis * 0.6:

break

else:

cur_dis += wave[1] - wave[0]

if i > 0:

wave = (wave_peaks[0][0], wave_peaks[i][1])

wave_peaks = wave_peaks[i + 1:]

wave_peaks.insert(0, wave)

# 去除车牌上的分隔点

point = wave_peaks[2]

if point[1] - point[0] < max_wave_dis / 3:

point_img = gray_img[:, point[0]:point[1]]

if np.mean(point_img) < 255 / 5:

wave_peaks.pop(2)

if len(wave_peaks) <= 6:

print("peak less 2:", len(wave_peaks))

continue

part_cards = seperate_card(gray_img, wave_peaks)

for i, part_card in enumerate(part_cards):

# 可能是固定车牌的铆钉

if np.mean(part_card) < 255 / 5:

print("a point")

continue

part_card_old = part_card

w = abs(part_card.shape[1] - SZ) // 2

part_card = cv2.copyMakeBorder(part_card, 0, 0, w, w, cv2.BORDER_CONSTANT,

value=[0, 0, 0])

part_card = cv2.resize(part_card, (SZ, SZ), interpolation=cv2.INTER_AREA)

word_images.append(part_card)

word_images_ = word_images.copy()

predict_result = template_matching(word_images_)

roi = card_img

card_color = color

print(predict_result)

break